Identification of a parametric loudspeaker system using an adaptive Volterra filter

Due to the parametric acoustic array effect in air, the input audible signal of a parametric loudspeaker system can be reproduced with high directivity at the target region. However, the reproduced audible signal suffers from harmonic distortion, which is the by-product of nonlinear interaction betw...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Ji, Wei, Gan, Woon-Seng
مؤلفون آخرون: School of Electrical and Electronic Engineering
التنسيق: مقال
اللغة:English
منشور في: 2013
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/98641
http://hdl.handle.net/10220/12520
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص:Due to the parametric acoustic array effect in air, the input audible signal of a parametric loudspeaker system can be reproduced with high directivity at the target region. However, the reproduced audible signal suffers from harmonic distortion, which is the by-product of nonlinear interaction between the primary waves. In order to investigate this inherent nonlinear phenomenon, a nonlinear system identification model is developed based on an adaptive Volterra filter. Unlike the conventional loudspeaker, the nonlinear characteristic of a parametric loudspeaker system is dependent on several primary parameters in nonlinear acoustics, which include the initial pressure of the primary waves, the observing distance and angle, as well as ambient temperature and relative humidity. By using a truncated Volterra series up to the 2nd-order kernel, numerical simulations are conducted to develop a system model with one group of parameters and examine the quadratic nonlinear intensity for different parameters’ settings. Experimental measurements, which take into account of emitter’s response, are carried out to verify the modeling result and evaluate the model performance. Based on the Volterra system model, the sound pressure level and the harmonic distortion can be accurately predicted.